Author: Gao, Xiang
Title: Bidding strategy of distributed energy resources participating in electricity market as price-makers
Advisors: Chan, K. W. Kevin (EE)
Degree: Ph.D.
Year: 2021
Subject: Renewable energy sources
Electric power distribution
Electric utilities -- Rates
Letting of contracts
Hong Kong Polytechnic University -- Dissertations
Department: Department of Electrical Engineering
Pages: xiii, 125 pages : color illustrations
Language: English
Abstract: Over the last decade, the world has witnessed a rapid growth in the capacity of distributed energy resources (DERs). The rapid expansion of their scales and their green contribution to energy conservation and environment protection have resulted them playing increasingly important roles in the electricity market. While those DERs can be aggregated to increase their own profits via strategically bidding, an effective approach to model their market behaviors shall also be developed to investigate their systemwide impacts. Wind power producers (WPPs) as aggregated large quantities of distributed wind turbines have occupied a dominant position in electricity generation of renewable DERs. Meanwhile, rapidly growing number of distributed electric vehicles (EVs) could be aggregated as a new demand response (DR) resource for providing energy through a coordinator called the EV aggregator, which dispatches the charging of EVs and exchanges information between the electricity market and individual EV owners. In recent years, there also exists a rapid increase in generating electricity by natural gas, which is expected to overtake coal by 2030 due to its lower price, less pollution, and higher energy conversion efficiency. The proliferation of natural gas generating units (NGGs) and the emerging of power-to-gas conversion (P2G) technology have enabled the bidirectional energy flows between the electric power and natural gas systems via integrated NGG-P2G facilities. However, DERs such as wind energy and EVs are easily influenced by the weather or depend on human behaviors, there are uncertainties in their outputs which would further influence their profits. The resulted bidding problem would therefore be an optimization problem involving uncertainties, and a stochastic optimization method is adopted in this thesis to handle this problem. Also, large-scaled DERs could be cooperated with or competed against each other to influence electricity prices according to their own interests, this thesis would conduct studies and develop bidding strategies for large-scaled DERs in the electricity market. For the bidding strategy of same-commodity cooperative arbitrage, this thesis firstly proposes a bi-level stochastic optimization model for an aggregated WPP-EV hybrid power plant (HPP) as a price-maker in the day-ahead (DA) market with consideration of the uncertainties of the wind power capacity and the electricity price in the real-time (RT) market. The profit of HPP is maximized in the upper level using the conditional­value-at-risk (CVaR) to manage the risk of the expected revenue, while the lower level is used to maximize the social welfare from the perspective of the grid. The formulated bi-level model is first transformed into a single-level mathematical program with equilibrium constraints (MPEC) and then further transformed to a mixed-integer linear programming (MILP) problem for solution. Simulation results have demonstrated the effectiveness of the proposed HPP model with strategically bidding prices to increase the profits and reduce its volatility caused by uncertainties through considering the risk-metric.
As to cross-commodity cooperative arbitrage, the integrated energy system has attracted more and more attention in recent years as it is beneficial to use the synergies between electricity and other energies for balancing the fluctuation of renewable DERs. In this thesis, a bidding strategy is developed for a coordinated WPP and NGG-P2G facility in DA market and RT market as well as providing auxiliary services employed in real-time. The WPP and NGG-P2G unit are coordinately operated in a virtual multi-energy plant (VMP) with the feature of natural gas, heat and electricity integration. A bi-level stochastic optimization model is proposed to determine the bidding strategy. The profit of the coordinated unit is maximized in the upper level with consideration of the uncertainties of WPP output and RT electricity prices, while the lower level is used to maximize social welfare from the perspective of the grid. Simulation results have demonstrated that uncertainties of the WPP are mitigated with flexible operation of NGG-P2G unit and the waste of wind resources is reduced, which is more profitable and environmentally friendly. The payoff in the proposed model is mostly provided by the WPP in DA market, where the integrated P2G-G2P unit enhances payoffs further by providing auxiliary services. Although numerous cooperative models have been demonstrated to bring more revenue with strategical bidding based on collaborators' compensation for peak shaving. They depend highly on the centralized control and scheduling of a central aggregator, in which the privacy of players cannot be guaranteed, and have little flexibility to cope with any self-decisions. Considering the bidding strategy with decentralized control which respects personal privacy and autonomy of energy suppliers, a competitive model is further formulated for WPPs and EV aggregators in a pool-based day-ahead electricity market. A bi-level multi-agent-based model is proposed to study their bidding behaviors, with market-clearing completion in the lower level and revenue maximization in the upper level. A stochastic framework is developed to incorporate the uncertainties in bid prices of other participants and the power production of WPPs and EV aggregators. The process of bidding decision is formulated as a stochastic game with incomplete information. Their lack of information in this stochastic market environment is counterbalanced by a multi-agent reinforcement learning (MARL) algorithm named win or learn fast policy hill climbing (WoLF-PHC) with maximizing their own profits by self-game. The feasibility and effectiveness of the proposed model and the WoLF-PHC solution approach are successfully illustrated using IEEE 6-bus and 118-bus systems. Multiple participants could respectively optimize their bids by learning with using the WoLF-PHC algorithm in competitive markets. Besides, compared with the cooperative model of the WPP and EV aggregator in the previous study, the proposed competitive model can adapt to a more flexible market environment in which every strategic player has full autonomy in biddings with incomplete information to maximize its own profit.
Rights: All rights reserved
Access: open access

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